Turn any technical book, document folder, or collection of sources into a unified agent skill — ready to study, reference, and use while you work in GitHub Copilot CLI, Amp, Claude Code, Codex, Hermes Agent, OpenCode, or OpenClaw.
24×–51× fewer tokens than dumping the book into context to answer one question, measured on real books (how it's measured).
How it works, in 3 steps:
Point it at a file, folder, or glob — /book-to-skill ./my-book.pdf
It distills the book into a skill — frameworks, decision rules, anti-patterns, and per-chapter files. Structure, not a summary.
Your agent loads it on demand — ask /my-book replication and it reads the right chapter and answers from the real content, no hallucination.
🤔 Why
You buy a great technical book. You read it once. Three months later you can't remember chapter 7 existed.
The usual workarounds don't help:
📄 "Let me just search the PDF" → you get a list of pages, not answers
🧠 "I'll ask the agent about this book" → it either hallucinates or says it doesn't have the content
📝 "I'll take notes as I read" → you end up with a 200-line doc you never open again
book-to-skill solves this by turning the book into a structured skill your agent loads on demand.
Once installed, you just type /your-book-slug replication and the agent reads the right chapter and answers from the actual content. No hallucination. No digging through PDFs. The book becomes part of your workflow.
Works with any host that supports the open Agent Skills standard — GitHub Copilot CLI, Amp, Claude Code, Codex, Hermes Agent, OpenCode, and OpenClaw all read the same SKILL.md format.
📦 What it generates
Running /book-to-skill your-book.pdf (or a folder, glob, or list of files) creates a full skill in the user-level cross-agent skills directory ~/.agents/skills/<slug>/ — one copy that Copilot CLI, Amp, Codex, OpenCode, and OpenClaw discover natively (OpenCode also reads its own managed root ~/.config/opencode/skills/<slug>/; OpenClaw also discovers it when using its default state directory, with a non-default OPENCLAW_STATE_DIR use the active state's skills/ root instead). When run under Claude Code, the converter also attempts a verified symlink at ~/.claude/skills/<slug>/ — Claude Code sees the skill when the link read-back confirms it; otherwise the run report says so. Hermes Agent partitions its personal skills by category and does not scan the cross-agent root, so a Hermes install lands in /skills/<category>/<slug>/ instead. OpenClaw-managed, OpenCode-managed, and project-local destinations remain available when explicitly requested:
File
Purpose
Size
SKILL.md
Core mental models + chapter index
~4,000 tokens
chapters/ch01-*.md …
One file per chapter, loaded on-demand
~1,000 tokens each
glossary.md
Every key term, alphabetically sorted with chapter refs
~1,500 tokens
patterns.md
All techniques, algorithms, and design patterns
~2,000 tokens
cheatsheet.md
Decision tables and quick-reference rules
~1,000 tokens
Chapter files are loaded on-demand — they don't count against the skill budget until you ask about that topic.
🏢 Beyond books
The name says "book", but the input is any structured prose. The same extraction works on knowledge you own and re-read constantly:
Internal documentation — architecture decision records, runbooks, onboarding guides. Fold a whole docs/ folder into one skill and ask it while you code.
Brand & design systems — voice guidelines, tone-of-voice docs, component principles. Turn a brand book into a skill your team queries instead of skimming a 60-page PDF.
Research clusters — a stack of papers plus your own notes, merged into a single unified skill and updated as new material lands (see Update / fold-in).
Specs & standards — RFCs, API contracts, compliance docs you reference but never memorize.
If you re-open a document often enough to wish you'd memorized it, it's a candidate.
🧾 The Discovery Loop Tax
A PDF-reading agent doesn't just read — it navigates: it re-fetches the ToC, backtracks, and re-processes all of it on every turn. book-to-skill pays that structuring cost once, at conversion, so queries stay proportional to the answer — 24×–51× fewer tokens than dumping the book into context, measured on real books.
Two halves: a deterministic Python extractor (document → clean text + metadata) and a spec-driven generator (your agent follows SKILL.md to turn that into a structured skill). On-demand chapter files keep the loaded skill small.
/book-to-skill <path|folder|glob> [skill-name] — plus analyze-only, generate-from-analysis, and update/fold-in modes. After a conversion, the converter can publish the skill to GitHub (private by default) so any host installs it with npx skills add.
💬 In practice → use cases — a DevEx book became a survey of 300+ engineers; a scanned PDF that stalled became #130. Add yours: the account lives in your own Gist, the index takes a one-line PR.
📥 Install
# One command, any host — via the cross-agent skills CLI: npx skills add virgiliojr94/book-to-skill
# Or manually — clone into your skills folder (registers /book-to-skill): git clone https://github.com/virgiliojr94/book-to-skill.git ~/.claude/skills/book-to-skill # (Copilot CLI: ~/.copilot/skills/ · Amp/cross-agent: ~/.agents/skills/) # (Hermes Agent: ${HERMES_HOME:-$HOME/.hermes}/skills/<category>/) # (OpenClaw: ${OPENCLAW_STATE_DIR:-$HOME/.openclaw}/skills/; ~/.agents/skills/ only with default state) # (OpenCode: ~/.agents/skills/ — it also reads ~/.config/opencode/skills/)
📥 All hosts, optional extractors, and the standalone CLI → docs/install.md
❓ FAQ
Common questions — "why not just dump the PDF?", cost, privacy, non-book inputs, multi-file books.
The extractor tries tools in order per format and uses the first available. If nothing is installed, it tells you which command to run. Plain text, Markdown, reStructuredText and AsciiDoc need no extra deps.
Check your setup in one command:python3 scripts/extract.py --check prints which extractors are installed for every format and the exact command to install anything missing — no file needed.
PDF — choose by book type:
Book type
Tool
Install
Speed
Text-heavy (prose, few tables)
pdftotext (poppler)
sudo apt install poppler-utils
⚡ instant
Text-heavy fallback
pypdf
pip3 install pypdf
⚡ instant
Text-heavy fallback
pdfminer.six
pip3 install pdfminer.six
⚡ instant
Technical (code, tables, formulas)
docling
pip3 install docling
~1.5s/page
Before extraction begins, the skill asks you whether the book is technical or text-heavy and picks the right tool automatically. Docling preserves markdown tables and code blocks; pdftotext is faster for prose-only books.
Scanned PDFs need OCR first. A PDF that is page images with no text layer — a photographed or scanned book — has nothing for these tools to extract. The extractor checks the first pages and stops immediately with an explanation, rather than working through the whole book to produce an empty skill. Run OCR yourself, then convert the result:
book-to-skill ships no book content — not a single page. It's a converter you point at files you already own.
Processing is local. Extraction and analysis run on your machine. Your files are never uploaded by this tool. (If your agent's model runs in the cloud, the text you feed it follows that provider's normal data terms — same as any prompt.)
You use your own copy. Bring a book you bought, docs your company owns, or papers you have the right to read.
The output is your notes. A generated skill is a structured, synthesized derivative — framework names, definitions, takeaways — not a reproduction of the text. The skill explicitly never copies raw passages (see Quality Rule #7). Treat it like handwritten study notes: yours, for personal use.
Don't redistribute. Publishing or sharing a generated skill of a copyrighted work can infringe the rights holder. Keep skills of third-party books private. Internal docs, your own writing, and openly-licensed material are fine to share within the bounds of their license.
When in doubt, follow the license or terms of the source document. This project is a tool; how you use it is on you.
💖 Sponsors
book-to-skill is free and MIT-licensed, maintained on personal time. If it saves you tokens or study hours, consider sponsoring its upkeep: PR reviews, multilingual fixes, releases, and docs.
Every sponsor is listed in BACKERS.md. Thank you for keeping open, privacy-first tooling alive. ✨
License
MIT — applies to the converter (code + skill definition) in this repository, not to any book or document you process with it.
Install with your agent
Paste this prompt into your agent. It reads this page and does the setup for you.
prompt
Read https://aiagentslisting.com/skill/book-to-skill to learn what the "Book to Skill" skill does and how to install it. Install it for my coding agent as documented on that page, then confirm the skill is available and summarize what it can do.
Agents can also browse this directory over MCP at https://aiagentslisting.com/api/mcp
Install manually
Claude Code
Run the install command below in your terminal.
The skill files land in your ~/.claude/skills directory, laid out as shown.
Restart Claude Code or start a new session. Skills are discovered on startup.
Transform written knowledge into actionable agent skills by extracting structure — not producing summaries.
Philosophy
Books contain crystallized expertise: frameworks, principles, and techniques that took years to develop. This skill extracts that knowledge into a format GitHub Copilot CLI, Amp, Claude Code, Hermes Agent, OpenCode, OpenClaw, or another compatible agent can leverage repeatedly.
Extract structure, not summaries. A skill isn't a book report. It's a toolkit of:
Named frameworks (mental models with clear application)
Actionable principles (rules that guide decisions)
Techniques (step-by-step methods)
Anti-patterns (what to avoid and why)
Voice calibration (how the author thinks and communicates)
Preserve the author's precision. Frameworks often have specific names for reasons. "The 5 Whys" isn't interchangeable with "ask why multiple times." Capture the exact formulation.
Layer depth appropriately. Simple books → simple skills. Complex books with 10+ frameworks → skills with reference files and on-demand chapters.
Modes of Operation
Four paths available. Route based on what the user asks:
1. Full Conversion (Default)
Trigger: User provides one or more document/directory/glob paths without special instructions
Action: Run all steps below (Steps 0–9)
Output: Complete skill with SKILL.md, chapters/, glossary, patterns, cheatsheet
2. Analyze Only
Trigger: User says "analyze", "just extract", or "I want to review before generating"
Action: Run Steps 0–3, then produce a structured extraction report (frameworks, principles, techniques found). Stop — do NOT generate skill files.
Output: Analysis report for user review
3. Generate from Prior Analysis
Trigger: User has existing analysis notes or previously ran analyze-only
Action: Skip Steps 0–3, use the provided analysis as input, run Steps 4–9
Output: Skill files from the provided analysis
4. Update / Fold-in (Existing Skill)
Trigger: User provides one or more new source paths and indicates they want to update an existing skill (either by pointing to the existing skill folder, providing a skill slug that already exists in SKILLS_HOME, or explicitly requesting an update).
Action: Run Step 0 (out-of-scope check), Step 1 (validate inputs), Step 1.5 (identify book type), and Step 2 (extract new files). Then skip to Step 5 (identify/detect existing skill path) and run the Update / Fold-in Workflow to merge the new content into the existing skill files.
Output: Updated existing skill with new/revised chapter summaries and merged indexes/glossaries.
Skill Locations
This converter can run from multiple skill systems. When looking for this converter's helper script or writing the generated book skill, prefer these locations in order:
GitHub Copilot CLI personal skills: ~/.copilot/skills/
Cross-agent personal skills (Copilot, Amp, Codex; OpenClaw with its default state): ~/.agents/skills/
Hermes Agent personal skills: $HERMES_HOME/skills/ (defaults to ~/.hermes/skills/)
Hermes Agent project skills: .hermes/skills/ or .agents/skills/
OpenClaw personal skills: ${OPENCLAW_STATE_DIR:-~/.openclaw}/skills/ (active state; ~/.agents/skills/ is shared only with the default state)
OpenClaw project skills: .agents/skills/ or skills/
OpenCode personal skills: ~/.config/opencode/skills/ or ~/.agents/skills/
OpenCode project skills: .opencode/skills/ or .agents/skills/
For generated book skills, prefer the user-level cross-agent root ~/.agents/skills/ — one physical copy serves the cross-agent hosts and OpenClaw when it uses its default state. Copilot CLI and Amp discover it natively; Claude Code needs a symlink from ~/.claude/skills/<skill_name> (created in Step 10, see Step 5 for the rules). Pick a host-private or project-local root only when the user explicitly asks for one. BOOK_TO_SKILL_SCOPE=project or personal can make that choice explicit for automation; do not ask a mandatory scope question merely because both scopes are available.
Step 0 — Out-of-scope check
If no arguments are provided, stop and respond:
"book-to-skill requires a supported document path, folder, or glob pattern. Usage: book-to-skill <path-to-document-folder-or-glob>... [skill-name-slug]"
Throughout the workflow:
Resolve the destination root first. Before any existing-skill lookup, determine where this run would write: the personal or project-local root for the user's host, per the Step 5 table and selection rules — a project-local request uses its project-local root, never the personal root. Below, "the resolved root" means that destination.
Identify the input paths and the optional skill slug.
If the last argument is not a file, folder, or glob that exists or matches any files, and it looks like a skill slug (e.g. lowercase hyphens, alphanumeric), treat it as SKILL_NAME.
Treat all other arguments as the list of INPUT_PATHS.
If any input path is an existing skill directory (contains SKILL.md and a chapters/ sub-folder), or if SKILL_NAME matches an existing skill slug in the resolved root, flag this run as an Update/Fold-in operation (Mode 4).
Re-run guard. Before starting extraction for a Full Conversion, derive the prospective skill slug under Step 5's naming options — the author-concept form, the by-title form, and any SKILL_NAME given — and check the resolved root for an existing match. On a match, STOP and ask the user: "<skill-name> already exists. Choose: (1) Update/Fold-in (Mode 4), (2) verify the existing skill is complete and stop, or (3) force full regeneration." Do not re-extract until the user chooses. On context recovery after an interrupted run (network cut-off, replayed or continued conversation), reuse the extraction work directory that run reported (Workdir -> in its output, or the path you set in BOOK_SKILL_WORKDIR) only if it is still intact and still matches the current inputs and options — the directory exists, its metadata.json is readable, the sources it lists match the files being converted now one-to-one on filename and content fingerprint (the extractor records a per-source sha256 in metadata.json; recompute the fingerprint from each file as it exists now and require an exact match — reuse_is_safe() in book_to_skill/utils.py is the executable form of this check), and its recorded extraction_mode is the mode this run is using (Step 2's "confirm the extraction is the document you asked for" rule). If it is missing (temp cleanup), its metadata.json is gone, the sources no longer match (a different filename, or a changed content fingerprint), the recorded metadata has no sha256 for a source (recorded before fingerprints existed, so freshness cannot be established), or the mode differs, say so and start a fresh extraction instead of resuming. When in doubt, ask the user before discarding or resuming.
Step 1 — Validate input
Verify that there is at least one supported file, directory, or glob pattern among the INPUT_PATHS.
For directories and globs, expand them to find matching supported files (.pdf, .epub, .docx, .txt, .md, .markdown, .rst, .adoc, .html, .htm, .rtf, .mobi, .azw, .azw3).
If no supported files are found, stop with a clear error message.
Step 1.5 — Identify content type
Before extracting, ask the user:
"What kind of content do these sources have? This helps me choose the best extraction method.
Text-heavy — mostly prose, few or no tables/code (e.g. management, productivity, narrative non-fiction)
Not sure — I'll use the fast method and warn you if quality seems limited"
Store the answer as BOOK_TYPE:
Option 1 → BOOK_TYPE=technical
Option 2 → BOOK_TYPE=text
Option 3 → BOOK_TYPE=text
If BOOK_TYPE=technical, inform the user before proceeding:
"📐 Technical mode selected — PDFs attempt Docling for layout-aware extraction. EPUBs and other formats use their existing text extractors; code, tables, and formulas may lose structure. Check each source's extraction_method and fallback_reason in metadata.json for the actual extraction path."
If BOOK_TYPE=text, inform:
"📄 Text mode selected — using the fastest suitable extractor for each file type. Plain text/Markdown/HTML are usually ready in seconds; PDFs use pdftotext when available."
Step 2 — Extract text from the source documents
Run the extraction script, passing the input paths:
SCRIPT_PATH="" HERMES_HOME_RESOLVED="${HERMES_HOME:-$HOME/.hermes}" OPENCLAW_STATE_DIR_RESOLVED="${OPENCLAW_STATE_DIR:-$HOME/.openclaw}" PROJECT_ROOT="$(git rev-parse --show-toplevel 2>/dev/null || true)" HERMES_PROJECT_TRUSTED=false if [ -n "$PROJECT_ROOT" ] && [ "${HERMES_AGENT:-}" = true ] && \ command -v hermes >/dev/null 2>&1 && \ command -v python3 >/dev/null 2>&1 && \ hermes config get skills.trusted_project_dirs --json 2>/dev/null | PROJECT_ROOT="$PROJECT_ROOT" python3 -c 'import json, os, pathlib, sys; root=pathlib.Path(os.environ["PROJECT_ROOT"]).resolve(); sys.exit(not any(pathlib.Path(p).expanduser().resolve() == root for p in json.load(sys.stdin)))' 2>/dev/null then HERMES_PROJECT_TRUSTED=true fi
CANDIDATES=( "$HOME/.copilot/skills/book-to-skill/scripts/extract.py" "$HOME/.agents/skills/book-to-skill/scripts/extract.py" "$HOME/.claude/skills/book-to-skill/scripts/extract.py" "${OPENCLAW_STATE_DIR_RESOLVED}/skills/book-to-skill/scripts/extract.py" "${OPENCLAW_STATE_DIR_RESOLVED}/skills"/*/book-to-skill/scripts/extract.py "${OPENCLAW_STATE_DIR_RESOLVED}/skills"/*/*/book-to-skill/scripts/extract.py "${OPENCLAW_STATE_DIR_RESOLVED}/skills"/*/*/*/book-to-skill/scripts/extract.py "${OPENCLAW_STATE_DIR_RESOLVED}/skills"/*/*/*/*/book-to-skill/scripts/extract.py "${OPENCLAW_STATE_DIR_RESOLVED}/skills"/*/*/*/*/*/book-to-skill/scripts/extract.py "${OPENCLAW_STATE_DIR_RESOLVED}/skills"/*/*/*/*/*/*/book-to-skill/scripts/extract.py "$HOME/.config/opencode/skills/book-to-skill/scripts/extract.py" "$HERMES_HOME_RESOLVED/skills/book-to-skill/scripts/extract.py" "$HERMES_HOME_RESOLVED"/skills/*/book-to-skill/scripts/extract.py ) if [ "${HERMES_AGENT:-}" != true ]; then # Project-local roots are resolved against the git worktree, not the current # directory: an agent can be invoked from anywhere inside the project (e.g. # `src/nested`) and these roots still have to be found — that is how OpenCode # and the other hosts discover project skills. The CWD-relative forms are kept # so the probe also works outside a git repository. if [ -n "$PROJECT_ROOT" ]; then CANDIDATES+=( "$PROJECT_ROOT/.github/skills/book-to-skill/scripts/extract.py" "$PROJECT_ROOT/.claude/skills/book-to-skill/scripts/extract.py" "$PROJECT_ROOT/.agents/skills/book-to-skill/scripts/extract.py" "$PROJECT_ROOT/.opencode/skills/book-to-skill/scripts/extract.py" ) fi CANDIDATES+=( ".github/skills/book-to-skill/scripts/extract.py" ".claude/skills/book-to-skill/scripts/extract.py" ".agents/skills/book-to-skill/scripts/extract.py" "skills/book-to-skill/scripts/extract.py" "skills"/*/book-to-skill/scripts/extract.py "skills"/*/*/book-to-skill/scripts/extract.py "skills"/*/*/*/book-to-skill/scripts/extract.py "skills"/*/*/*/*/book-to-skill/scripts/extract.py "skills"/*/*/*/*/*/book-to-skill/scripts/extract.py "skills"/*/*/*/*/*/*/book-to-skill/scripts/extract.py ".opencode/skills/book-to-skill/scripts/extract.py" ) if [ -n "$PROJECT_ROOT" ]; then CANDIDATES+=( "$PROJECT_ROOT/skills/book-to-skill/scripts/extract.py" "$PROJECT_ROOT/skills"/*/book-to-skill/scripts/extract.py "$PROJECT_ROOT/skills"/*/*/book-to-skill/scripts/extract.py "$PROJECT_ROOT/skills"/*/*/*/book-to-skill/scripts/extract.py "$PROJECT_ROOT/skills"/*/*/*/*/book-to-skill/scripts/extract.py "$PROJECT_ROOT/skills"/*/*/*/*/*/book-to-skill/scripts/extract.py "$PROJECT_ROOT/skills"/*/*/*/*/*/*/book-to-skill/scripts/extract.py ) fi fi CANDIDATES+=( "$HOME/.config/agents/skills/book-to-skill/scripts/extract.py" "$HOME/.config/amp/skills/book-to-skill/scripts/extract.py" ) if [ "$HERMES_PROJECT_TRUSTED" = true ]; then CANDIDATES=( "$PROJECT_ROOT/.hermes/skills/book-to-skill/scripts/extract.py" "$PROJECT_ROOT/.hermes/skills"/*/book-to-skill/scripts/extract.py "$PROJECT_ROOT/.agents/skills/book-to-skill/scripts/extract.py" "$PROJECT_ROOT/.agents/skills"/*/book-to-skill/scripts/extract.py "${CANDIDATES[@]}" ) fi for candidate in "${CANDIDATES[@]}" do if [ -f "$candidate" ]; then SCRIPT_PATH="$candidate" break fi done
if [ -z "$SCRIPT_PATH" ]; then echo "Could not find scripts/extract.py for book-to-skill" >&2 exit 1 fi
PYTHON_BIN="${PYTHON_BIN:-python3}" if ! command -v "$PYTHON_BIN" >/dev/null 2>&1; then PYTHON_BIN="python" fi
Before extraction, the script checks optional Python packages needed for the detected format. If a better extractor is missing, it prompts the user with the available fallback. Non-interactive sessions default to fallback unless install mode is explicitly yes.
Tip — preflight the environment: run "$PYTHON_BIN" "$SCRIPT_PATH" --check to print a per-format report of which extractors are installed and the exact command to install whatever is missing, without processing any file. Useful when a user reports a setup or quality problem.
This creates a per-run work directory — <tempdir>/book_skill_work-<pid>/ by default, or exactly the path you set in BOOK_SKILL_WORKDIR — containing:
full_text.txt — combined extracted text of all sources with clear visually demarcated boundaries.
metadata.json — overall combined size, words, pages, token counts, dropped EPUB image counts, the resolved workdir, and a detailed list of individual processed sources.
The run prints all three paths on completion (Workdir ->, Text ->, Meta ->). Take the paths from that output (or from metadata.json's own workdir field) rather than assuming a fixed location — the directory name differs per run so that concurrent extractions on one machine cannot overwrite each other's results.
Read that run's metadata.json to inspect the results.
Always confirm the extraction is the document you asked for before generating anything: check filename / source_file in metadata.json, or the SOURCE: header on the first line of full_text.txt. If you are waiting on a background run, wait on its specific workdir — polling a shared path can surface a different run's output.
Step 2.5 — Pre-flight cost estimate
Read this run's metadata.json (the Meta -> path from the extraction output) and present the user with an estimate before doing any generation:
📖 Sources detected: <total_sources> source(s)
<list each source filename and format from the sources metadata list>
<if images_dropped > 5: warn that N source images were not read>
📄 Combined Pages/Sections: ~<N> | Words: ~<N> | Total tokens: ~<N>K
💰 Estimated token cost (Full Conversion / Update):
Input (reading + prompts): ~<N>K tokens
Output (skill files generated/updated): ~<N>K tokens
Total: ~<N>K tokens
Cost: multiply the token counts above by your model's current
input/output per-1M-token rates (prices and model names change often —
do not hardcode them; quote today's rate and label it as an estimate).
⏱ Estimated time: ~<N> minutes
📁 Files to be generated/updated:
SKILL.md + chapter files + glossary + patterns + cheatsheet
➡ Proceed with Full Conversion / Update? (or type "analyze only" to preview first)
How to estimate:
Input tokens ≈ estimated_tokens from metadata × 1.3 (prompts overhead per chapter pass)
Per-chapter budget midpoint by BOOK_TYPE (DEPTH is decided later in Step 4 and can raise it): text ≈ 1,000, technical ≈ 1,800. If the user has already indicated reference-only vs deep study, use the matching row of the Step 7 matrix.
Cost: report the token counts and multiply by the user's current per-1M-token input/output rates. Do NOT hardcode dollar figures — model names and prices change; if you show one, label it an estimate and date it.
Wait for the user to confirm before proceeding. If they say "analyze only", switch to Mode 2.
Step 2.6 — REPL-style access for large books (> 50k tokens)
Inspired by the Recursive Language Model (RLM) paradigm: treat full_text.txt as a queryable corpus, not a single read. Loading the whole file into context burns budget you will need later for generation.
For books over ~50k tokens, prefer programmatic probes over Read(full_text.txt) without bounds:
# Size check before any Read wc -w "$FULL_TEXT_PATH"
# Find chapter offsets without loading the whole file grep -n -E "^\s*(Chapter|CHAPTER)\s+[0-9]+" "$FULL_TEXT_PATH" | head -40
# Pull only the chapter you need (lines start..end inclusive) sed -n '<start>,<end>p' "$FULL_TEXT_PATH"
# Verify a framework is actually mentioned before claiming it in SKILL.md grep -c -i "westrum\|dora" "$FULL_TEXT_PATH"
# Targeted Read with offset/limit avoids dumping the full file # Read(file_path=full_text.txt, offset=<line>, limit=<lines>)
Use this approach for Step 3 (structure analysis), Step 7 (per-chapter summaries), and Step 8 (glossary / patterns extraction). On books under 50k tokens, a single Read is fine.
Why this matters: a 200-page book is ~75k tokens. Re-reading it once per chapter (28 passes) costs ~2M input tokens; using grep + sed to pull only relevant slices keeps generation cost proportional to the output, not the source.
Step 3 — Analyze book structure
Read the first 8,000 characters of the extracted full_text.txt to identify:
Book title and author(s)
Chapter structure (look for "Chapter N", "PART I", numbered headings, table of contents)
Core themes and subject domain
Approximate number of chapters
Then read the Table of Contents section if present to map all chapters.
If mode is "Analyze Only": produce the extraction report now and stop. Structure:
## Extraction Report — <Title>
### Author's Core Frameworks
- **<Framework Name>**: <what it is and when to apply>
### Key Principles
- <Principle>: <actionable rule>
### Techniques & Methods
- <Technique>: <step-by-step or how-to>
### Anti-patterns
- <What to avoid>: <why>
### Suggested Skill Name
`{author-lastname}-{core-concept}` — e.g. `cialdini-influence`
### Chapters Detected
| # | Title | Main Frameworks |
Step 4 — Ask purpose (Full Conversion only)
Before generating, ask the user:
"What should this skill help you do? (Pick one or more)
Apply the author's frameworks while working
Think with the author's mental models
Reference specific chapters and concepts
All of the above"
Use the answer to weight what gets highlighted in the SKILL.md Core section.
Derive DEPTH from the answer (no extra prompt):
Answer is only option 3 (reference) → DEPTH=reference — lean, fast-lookup chapters.
Answer includes option 1, 2, or 4 → DEPTH=study — deeper chapters with more worked detail, examples, and reasoning.
DEPTH and BOOK_TYPE together set the per-chapter token budget in Step 7. Do not ask a separate "study vs reference" question — it is inferred here. (In Modes 2/3, where Step 4 is skipped, default DEPTH=study.)
Step 5 — Determine skill name
If SKILL_NAME was provided, use it as the skill slug.
Otherwise, propose two options and let the user choose:
By author-concept: {author-lastname}-{core-concept} (e.g. cialdini-influence, meadows-systems)
By title: lowercase hyphens from book title (e.g. designing-data-intensive-apps)
Default to author-concept format if the book has a strong methodological identity.
Choose the destination skill root (SKILLS_HOME). First resolve scope from an explicit user request or BOOK_TO_SKILL_SCOPE, then probe host. A request for project-local/project output selects the project-local row; a request for personal/global output selects the personal row. If neither scope is requested, preserve the established personal default (~/.agents/skills for non-Hermes hosts). Do not ask a mandatory scope question solely because project-local roots exist. The selected root may still require host approval before writing.
$HERMES_HOME/skills/<category> (defaults to ~/.hermes/skills/<category>)
.hermes/skills/<category> → .agents/skills
Claude Code
~/.agents/skills + symlink from ~/.claude/skills/<skill_name>
.claude/skills
OpenClaw
${OPENCLAW_STATE_DIR:-~/.openclaw}/skills (active state; ~/.agents/skills only with default state)
.agents/skills → skills/
Hermes Agent is the one host that keeps its own personal root: it partitions personal skills by category and does not scan the cross-agent root. Use the active profile's HERMES_HOME and choose a category that matches the generated skill's subject. Do not construct profile paths manually. If the user selects a project-local Hermes root, run hermes skills trust <project-root> after generation and verify discovery with hermes skills list; project skills remain unavailable until the project is trusted.
For OpenClaw, use the active state directory's skills/ root: ${OPENCLAW_STATE_DIR:-~/.openclaw}/skills/. The shared ~/.agents/skills compatibility root is discoverable only when OPENCLAW_STATE_DIR is unset or the default ~/.openclaw; with a non-default state, do not claim that OpenClaw will see a shared-root install. Verify discovery with openclaw skills list after generation.
| OpenCode | ~/.agents/skills (discovered natively; ~/.config/opencode/skills and ~/.claude/skills are read too) | .opencode/skills → .agents/skills → .claude/skills |
Hermes Agent is the one host that keeps its own personal root: it partitions personal skills by category and does not scan the cross-agent root. Use the active profile's HERMES_HOME and choose a category that matches the generated skill's subject. Do not construct profile paths manually. If the user selects a project-local Hermes root, run hermes skills trust <project-root> after generation and verify discovery with hermes skills list; project skills remain unavailable until the project is trusted.
OpenCode scans the cross-agent ~/.agents/skills root natively, so the generated-skill default above already serves it — no symlink and no trust step. It also reads its own managed ~/.config/opencode/skills root and the Claude compatibility root ~/.claude/skills; use the OpenCode-managed root only when the user asks for it. Project skills are discovered by walking up from the working directory to the git worktree, so start a new session after generation if the skill does not appear. (~/.cache/opencode/skills is the remote-skill download cache from skills.urls, not an authoring root — never write a generated skill there.)
Selection rules:
Personal install: set SKILLS_HOME to ~/.agents/skills (create the directory if missing). One exception, so the default does not invent a convention in someone else's house: if ~/.agents/skills does not exist and the host's private root already contains skills, use the private root instead and say why in the report.
Claude Code does not scan ~/.agents/skills — after generation completes, Step 10 links the skill in with ln -sfn "$HOME/.agents/skills/<skill_name>" "$HOME/.claude/skills/<skill_name>".
Hermes Agent personal installs use the Hermes row above, not the cross-agent root, and take no symlink.
If the user explicitly asks for a host-private root (~/.copilot/skills, ~/.claude/skills, ~/.config/agents/skills, ~/.config/amp/skills), honor it and skip the symlink.
If the user explicitly asked for project-local output, use the project-local row for their host.
If the choice requires knowing the host (project-local output, the Hermes personal root, the OpenClaw state root, or the Claude Code symlink) and you cannot identify it, ask: "Which agent are you running in — OpenCode, OpenClaw, Hermes Agent, GitHub Copilot CLI, Amp, Codex, or Claude Code?"
For OpenClaw personal output, use ${OPENCLAW_STATE_DIR:-$HOME/.openclaw}/skills. The shared ~/.agents/skills root is a valid OpenClaw destination only when OPENCLAW_STATE_DIR is unset or equals the default $HOME/.openclaw; otherwise use the active state root or a project/extra directory.
If the user explicitly asks for an OpenClaw-managed personal root, use the active state root and verify discovery with openclaw skills list.
Set SKILLS_HOME to the selected root and check if $SKILLS_HOME/<skill_name>/ already exists. On Claude Code, also check whether ~/.claude/skills/<skill_name> exists as a real directory (not a symlink) — a previous install may live there; if so, offer to migrate it (move the directory into ~/.agents/skills/ and replace the original path with the symlink) before continuing.
If the skill already exists, prompt the user to choose:
Update / Fold-in (Mode 4) — integrate new files/content into the existing skill components.
Overwrite — delete and regenerate the skill from scratch.
Rename — append -2 or use a different custom slug.
If the user selects Update / Fold-in, proceed immediately to the Update / Fold-in Workflow section after Step 2.5 (skipping Steps 3, 4, 6, 7, 8, 9).
Step 6 — Create skill directory structure
mkdir -p "$SKILLS_HOME/<skill_name>/chapters"
Step 7 — Generate chapter summaries
TOKEN BUDGET RULE — CRITICAL (adaptive):
The per-chapter budget scales with BOOK_TYPE and DEPTH. Technical chapters need room for code and tables; study depth needs room for worked reasoning. Pick the budget from this matrix:
DEPTH=reference
DEPTH=study
BOOK_TYPE=text
800–1,200 tokens
1,000–1,800 tokens
BOOK_TYPE=technical
1,200–1,800 tokens
2,000–3,000 tokens
These are per-file targets, not hard caps — a dense chapter may run over, a thin one under. Density still beats length (Quality Rule #3): never pad to hit a number.
Files are loaded on-demand, so a larger chapter only costs tokens when that chapter is actually read.
When in doubt between two cells (e.g. mixed-content book), use the lower budget and let depth come from precision, not volume.
DEPTH=study is earned with content, not a bigger number. The standard section template (Core Idea → Connects To) naturally lands a dense prose chapter around 700–900 tokens. To reach the study budget honestly — not by padding — a study-depth chapter must add concrete material:
Reproduce one worked example or artifact from the chapter (e.g. the example press release, a sample dialogue, a filled-in template, a decision the author walks through) under a ## Worked Example section. This is the single biggest lever and the main thing a learner returns for.
Expand the "How" of each framework into explicit steps or criteria, not a one-liner.
Add a short "Why it works / failure mode" note to the top 1–2 frameworks.
If a chapter genuinely has no worked example and resists expansion, let it land below the study floor rather than padding — and note that the chapter is thin in its Core Idea. A reference-depth chapter, by contrast, deliberately omits worked examples and keeps only the decision-ready essentials.
For EACH chapter/major section identified in Step 3:
Read the corresponding section of the extracted full_text.txt using top-level chapter_ranges from metadata.json when present (start_line/end_line are 1-based inclusive, for sed/Read); otherwise grep for chapter headings. (sources[].chapter_ranges use the same fields but line numbers in that source's text only, not the consolidated full_text.txt.)
Create $SKILLS_HOME/<skill_name>/chapters/ch<NN>-<slug>.md using the structure below.
text → prioritize "Frameworks Introduced", "Mental Models", and "Key Takeaways"; skip empty technical sections
# Chapter N: <Full Title>
## Core Idea <1–2 sentences: the single most important thing this chapter teaches>
## Frameworks Introduced - **<Framework Name>**: <exact formulation — preserve the author's naming> - When to use: <specific situation> - How: <steps or criteria>
## Key Concepts - **<Term>**: <precise definition in 1 sentence> (5–10 most important terms from this chapter)
## Mental Models <2–4 frameworks or thinking tools. Write as "Use X when Y" or "Think of X as Y">
## Anti-patterns - **<What to avoid>**: <why it fails>
## Code Examples *(technical books only — omit if BOOK_TYPE=text)* <!-- Copy the most instructive snippet from the chapter. Preserve indentation exactly. --> ```<language> <key code example from this chapter>
What it demonstrates:
Reference Tables (technical books only — omit if BOOK_TYPE=text)
Worked Example (DEPTH=study only — omit for DEPTH=reference)
Key Takeaways
(3–7 takeaways a practitioner must remember)
Connects To
Ch N:
:
---
## Step 8 — Generate supporting files
### glossary.md
Create `$SKILLS_HOME/<skill_name>/glossary.md`:
- Every significant term from the book, alphabetically sorted
- Format: `**Term** — definition (Ch N)`
- Max 1,500 tokens
### patterns.md
Create `$SKILLS_HOME/<skill_name>/patterns.md`:
- All concrete techniques, design patterns, algorithms from the book
- Format: `## Pattern Name\n**When to use**: ...\n**How**: ...\n**Trade-offs**: ...`
- Max 2,000 tokens
### cheatsheet.md
Create `$SKILLS_HOME/<skill_name>/cheatsheet.md`:
**This is the most differentiated layer of the skill — treat it as a reasoning aid, not a keyword list.** Anyone can grep the glossary for a term. The cheatsheet captures the author's *judgment*: the decisions they'd make and why. It's the file that turns "I know the words" into "I'd act the way the author would".
Prioritize, in order:
1. **Decision rules** — "When X, do Y, because Z." The if/then logic the author applies, stated so the reader can apply it without re-reading the book.
2. **Decision trees / flowcharts** (as nested bullets or a small table) — for choices with more than two branches.
3. **Trade-off matrices** — competing options scored on the dimensions the author cares about, so the reader can pick under their own constraints.
4. **Thresholds & defaults** — the specific numbers, ratios, or rules of thumb the author commits to (e.g. "keep functions under ~20 lines", "alert when error budget < 10%").
5. **Tells & smells** — fast heuristics for recognizing a situation ("if you see X, you're probably in trouble Y").
Avoid: bare term→definition rows (that's the glossary), and prose paragraphs (that's the chapters). Every line should help the reader *decide* something.
- Format mostly as compact tables and decision rules; the content you'd want on a single printed page kept beside you while working.
- Max 1,200 tokens.
---
## Step 9 — Generate the master SKILL.md
**CRITICAL TOKEN BUDGET: Keep SKILL.md body under 4,000 tokens.**
Compaction truncates from the END — put the most important content FIRST.
Create `$SKILLS_HOME/<skill_name>/SKILL.md`:
```markdown
---
name: <skill_name>
description: "Knowledge base from \"<Full Title>\" by <Author(s)>. Use when applying <author>'s frameworks for <key topics, 3–6 terms>, studying the book, or referencing its concepts."
---
<!-- argument-hint: [topic, framework name, or chapter number] -->
# <Full Title>
**Author**: <Author(s)> | **Pages**: ~<N> | **Chapters**: <N> | **Generated**: <YYYY-MM-DD>
## How to Use This Skill
- **Without arguments** — load core frameworks for reference
- **With a topic** — ask about `replication`, `pricing`, or another indexed topic; I find and read the relevant chapter
- **With chapter** — ask for `ch05`; I load that specific chapter
- **Browse** — ask "what chapters do you have?" to see the full index
When you ask about a topic not covered in Core Frameworks below, I will read
the relevant chapter file before answering.
---
## Core Frameworks & Mental Models
<!-- ~2,000 tokens: the author's most important named frameworks and principles.
Preserve exact names. Write as "Use X when Y", "Prefer X over Y because Z".
This is a toolkit, not a summary. -->
<generate 2,000 tokens of the most critical frameworks and insights here>
---
## Chapter Index
| # | Title | Key Frameworks |
|---|-------|----------------|
| [ch01](chapters/ch01-<slug>.md) | <Title> | <framework1>, <framework2> |
| [ch02](chapters/ch02-<slug>.md) | <Title> | <framework1>, <framework2> |
...
## Topic Index
<!-- Alphabetical. Major terms/frameworks → chapter(s) that cover them. -->
- **<Term>** → ch<N>[, ch<N>]
- **<Term>** → ch<N>
## Supporting Files
- [glossary.md](glossary.md) — all key terms with definitions
- [patterns.md](patterns.md) — all techniques and design patterns
- [cheatsheet.md](cheatsheet.md) — quick reference tables and decision guides
- Full text: `<output_text from metadata.json>` — grep it for depth the chapter files omit (local extraction, not shipped)
---
## Scope & Limits
This skill covers the book content only. For hands-on implementation in your codebase,
combine with project-specific tools. For topics beyond this book, check related skills
or ask the agent directly.
<if images_dropped > 5: state that N source images were not read>
Step 9.5 — Scan the generated skill
Before reporting success, loading the skill in another session, or publishing it, run the advisory security scan:
If the scanner exits non-zero, stop and ask a human to review its file/line findings. Do not silently rewrite the generated files, and do not load or publish the skill until the findings are resolved or explicitly accepted.
Step 10 — Cleanup and report
If the host is Claude Code and SKILLS_HOME is ~/.agents/skills (the default personal install), expose the skill to Claude Code with a symlink — Claude Code only scans ~/.claude/skills:
mkdir -p "$HOME/.claude/skills" LINK="$HOME/.claude/skills/<skill_name>" TARGET="$HOME/.agents/skills/<skill_name>" if [ -d "$LINK" ] && [ ! -L "$LINK" ]; then CLAUDE_STATUS="skipped-realdir" # Step 5 migration declined; leave the old dir else ln -sfn "$TARGET" "$LINK" 2>/dev/null || true # Read the link back — do NOT trust that `ln` did what was asked. On Windows/MSYS # `ln -s` may COPY instead of link (or need Developer Mode / an elevated shell), and # PowerShell/cmd have no `ln` at all. The report must reflect what is on disk, not the # fact that the command ran. if [ -L "$LINK" ] && [ "$(readlink "$LINK")" = "$TARGET" ]; then CLAUDE_STATUS="linked" elif [ -e "$LINK" ]; then CLAUDE_STATUS="copy" # a real file/dir landed instead of a link else CLAUDE_STATUS="absent" # ln unavailable or refused fi fi
The real-directory guard is required: ln -sfn into an existing real directory would nest the link inside it (~/.claude/skills/<skill_name>/<skill_name>), leaving Claude Code loading the stale copy. If the user declined the Step 5 migration, skip the symlink and say so in the report — Claude Code keeps using the old directory until it is migrated.
Read the link back before you report anything about it. The symlink is a claim, not a fact: fill the "Discoverable by" line from CLAUDE_STATUS (what is actually on disk), never from "the command was issued". Do not hard-fail when the link is missing or is a copy — the skill exists at the hub and every other host still finds it; the honest report is "written to ~/.agents/skills/<skill_name>; Claude Code will not see it until the link is created", not an abort. (Windows lead, unverified: a directory junction — mklink /J in an elevated cmd, or New-Item -ItemType Junction in PowerShell — needs neither Developer Mode nor a symlink privilege; if you attempt it, it does not change the read-back-then-report rule.)
Skip this when the user chose a host-private or project-local root (Step 5, rules 3-4).
Then clean up the extraction workdir:
PYTHON_BIN="${PYTHON_BIN:-python3}" if ! command -v "$PYTHON_BIN" >/dev/null 2>&1; then PYTHON_BIN="python" fi
Remove **the work directory this run actually used** — the `Workdir ->` path from the extraction output, which is also stored as `workdir` in `metadata.json`. Never delete a directory you did not create: another extraction may be running beside yours.
```bash # WORKDIR is the path this run reported; quote it in case of spaces. rm -rf "$WORKDIR"
Equivalently, if you still have the metadata file:
Older copies of this file removed a single fixed book_skill_work directory. That path is
no longer used, so such a cleanup is now a harmless no-op rather than something that could
delete a concurrent run's output.
Then report to the user:
✅ Skill created: $SKILLS_HOME/<skill_name>/
📚 Book: <Full Title> — <Author>
📄 Pages: ~<N> | Chapters: <N>
Files generated:
SKILL.md — core frameworks + index (~X tokens)
chapters/ — <N> chapter summaries (~X tokens each, ~X total)
glossary.md — key terms (~X tokens)
patterns.md — techniques & patterns (~X tokens)
cheatsheet.md — quick reference (~X tokens)
─────────────────────────────────────────────────────
Total skill size: ~X tokens (loaded on-demand, not all at once)
💡 Tip: check your agent's session cost/usage command to see actual token usage.
Usage:
Ask for <skill_name> → load core frameworks
Ask <skill_name> about <topic> → find and explain a topic
Ask <skill_name> for ch<N> → dive into a specific chapter
Discoverable by: <only what is true for the chosen destination — see below>
Somewhere else? mv ~/.agents/skills/<skill_name> <dest_root>/<skill_name> \
&& ln -sfn <dest_root>/<skill_name> ~/.claude/skills/<skill_name>
Prompted for permission on every file? That is your host gating writes outside the
working directory. Say "save it in this project" and re-run to write inside it.
Reload (if your agent doesn't auto-detect new skills):
GitHub Copilot CLI: /skills reload
Claude Code: restart the session
Amp: restart the session
Hermes Agent: start a new session
OpenClaw: openclaw skills list (new session if watcher disabled)
OpenCode: start a new session
Share this skill (optional):
GitHub repo, installable on any host (Step 11): say "publish"
Copilot ecosystem: gh skill publish $SKILLS_HOME/<skill_name>
Fill the "Discoverable by" line from CLAUDE_STATUS (the read-back result), never from the fact that ln ran — for ~/.agents/skills installs:
linked → "Copilot CLI, Amp, Codex (natively); Claude Code via symlink ~/.claude/skills/<skill_name>"
skipped-realdir → "Copilot CLI, Amp, Codex (natively); NOT Claude Code — migrate the real directory at ~/.claude/skills/<skill_name> first"
copy or absent → "Copilot CLI, Amp, Codex (natively); NOT Claude Code — the host could not create the symlink (a plain copy drifts on the next Update/Fold-in). Enable Developer Mode / create the link manually, or run the skill from ~/.agents/skills"
Hermes Agent personal root → "Hermes Agent (from $HERMES_HOME/skills/<category>)"; no symlink claim, and no cross-agent claim, because the other hosts do not scan the Hermes root
other host-private or project-local root → name only the host(s) that scan that root; no symlink claim
The "Somewhere else?" relocation line must be correct for the path actually taken, so it never breaks the symlink the run just created. mv always targets the final skill directory, <dest_root>/<skill_name>, never <dest_root> itself.mv ~/.agents/skills/mybook ~/.copilot/skills && ln -sfn ~/.copilot/skills ~/.claude/skills/mybook reads as valid and is not: the skill lands at ~/.copilot/skills/mybook while the link points one level up at the root, so Claude Code resolves to a directory with no SKILL.md, which is the exact breakage this line exists to avoid. Substitute the destination the user actually named, so the printed command carries real paths and there is nothing left to interpret:
The "Prompted for permission on every file?" line is the answer to a host that gates writes outside the working directory (any personal-scope root is out-of-cwd): the destination was announced above, and the one-line fix — re-run asking for the project-local root — sits next to it. Keep it only for personal-scope installs; drop it when the user already chose project-local.
Step 11 — Publish the generated skill to GitHub (optional)
After the Step 10 report, offer once — and only if the Step 9.5 scan passed:
"Want me to publish this skill to GitHub so any Agent Skills host can install it with npx skills add? (yes / skip)"
If the user declines, stop here. Requirements: the gh CLI, authenticated (check gh auth status). If gh is missing or unauthenticated, offer to set it up (brew install gh or https://cli.github.com, then gh auth login) — or use the no-gh path: the user creates an empty repo of the chosen visibility in the GitHub web UI, then you run the git init/add/commit commands below followed by git remote add origin <repo-url> && git push -u origin main. The visibility rule below applies to the web-created repo exactly the same.
Visibility is a separate closed question — never inferred, never read out of an earlier answer. Once the user accepts, ask it on its own and require a one-word reply:
"Private or public repository? Reply with one word: private or public."
The reply must bepublic, not merely contain it — a hard rule, not a suggestion. Run gh repo create with --private in every case except one: the answer to the visibility question is the bare word public. Substring matching is forbidden, because a sentence about the source's licence is not a visibility answer — "it's public domain", "the book is public domain", "it's publicly available" all describe the material, not the repository, and all resolve to --private. A paraphrase, a sentence, an ambiguous answer, silence, or your own inference is NOT consent: re-ask once, and if the reply is still not the bare word, use --private and say so in the report. A private repo can be flipped public later; a public push of book-derived content cannot be un-published.
Copyright gate — always apply before creating the repo: chapter files are synthesized summaries, not raw text, but they still derive from the source material. Per the README's Copyright & fair use policy, skills generated from third-party copyrighted books must stay private; offer public only when the source is the user's own writing, openly licensed content, or material the user explicitly confirms they are authorized to redistribute publicly — and state which case applies. Having access to internal company material is not permission to disclose it: skills from internal docs stay private unless the user states they hold publication rights.
If accepted:
Add a repo README.md inside $SKILLS_HOME/<skill_name>/ (never overwrite an existing file) — the skill title, a one-paragraph description ("Agent skill generated from by <Author> with book-to-skill"), the install command from step 3 below, the file inventory, and a note that the content is synthesized summaries, not the book text.
Initialize the skill folder as a git repository and create the remote (default repo name <skill_name>; let the user override — some prefer a <skill_name>-skill suffix). Nested-repo guard: first check whether the skill folder already sits inside a git repository (git -C "$SKILLS_HOME/<skill_name>" rev-parse --show-toplevel — always the case for project-local roots like .claude/skills/). If it does, do NOT git init in place: the outer repository would record the folder as an embedded repo (gitlink, mode 160000) without .gitmodules, and fresh clones of the outer project would silently omit the skill. Instead, copy the skill folder to a scratch directory, run the commands below from the copy, and tell the user the published repo — not the project-local folder — is the remote's working copy.
cd "$SKILLS_HOME/<skill_name>" git init -b main git add -A git commit -m "Add <skill_name> skill" gh repo create <repo_name> --private --source . --push # --private is the default; substitute --public ONLY under the visibility rule above # (the visibility answer WAS the bare word "public" AND the copyright gate allows it)
Report the repo URL and the cross-host install command:
✅ Published: https://github.com/<owner>/<repo_name> (<private|public>)
Install on any Agent Skills host:
npx skills add https://github.com/<owner>/<repo_name> --skill <skill_name>
When the nested-repo guard fired and the repo was published from a scratch copy, add one line — that local folder never gains a remote, so the Update/Fold-in push offer will never appear for it:
⚠️ Published from a copy: <skill folder> sits inside another git repository, so it has
no remote of its own. To publish a later update, re-run Step 11, or clone
https://github.com/<owner>/<repo_name> and fold new material into the clone.
The root-level SKILL.md layout is exactly what the skills CLI detects, so the repo is installable as-is — no restructuring needed. Outside the nested-repo case the local folder stays the live install for this machine and is the remote's working copy, so later Update/Fold-in runs can commit and push their changes to the same remote.
Update / Fold-in Workflow
When performing an Update/Fold-in operation on an existing skill at $SKILLS_HOME/<skill_name>/:
1. Read Existing Skill Structure
Read and parse the existing skill's files:
Read $SKILLS_HOME/<skill_name>/SKILL.md to parse the existing Chapter Index, Topic Index, metadata (author, total chapters), and Core Frameworks.
List all files in $SKILLS_HOME/<skill_name>/chapters/ to find the highest chapter number (e.g. ch12).
Read $SKILLS_HOME/<skill_name>/glossary.md, $SKILLS_HOME/<skill_name>/patterns.md, and $SKILLS_HOME/<skill_name>/cheatsheet.md to see what terms and frameworks are already indexed.
2. Match Content & Identify Revisions vs. Additions
Analyze the new extracted text in this run's full_text.txt (the Text -> path from the extraction output) to identify if the new content represents:
Updates/Revisions to existing chapters: If a section of the new content directly updates or expands an existing chapter's topic, read the existing chapter file, merge the new details into it, and rewrite the file.
New additions: If the content introduces new chapters, papers, or separate sections, create new chapter summary files under chapters/. Start numbering these files after the highest existing chapter number (e.g. if the existing chapters stop at ch12, create ch13-*.md, ch14-*.md, etc.).
3. Generate or Update Chapter Summary Files
For each new or revised chapter:
Read the corresponding section of the extracted new text.
Follow the formatting guidelines in Step 7 to build the summary.
Write/update the file in $SKILLS_HOME/<skill_name>/chapters/.
4. Merge Supporting Files
Merge glossary.md:
Read the existing $SKILLS_HOME/<skill_name>/glossary.md.
Extract all new terms and definitions from the new content (Step 8 glossary guidelines).
Combine and alphabetize the list of existing and new terms.
If a term already exists, append the new chapter/source references to it (e.g. **Term** — definition (Ch 4, Ch 13)).
Rewrite $SKILLS_HOME/<skill_name>/glossary.md with the fully merged, alphabetized list.
Extract new comparison rules, decision tables, or parameter guides.
Integrate them cleanly into the cheatsheet structure.
5. Re-generate the Master SKILL.md
Update the master skill file $SKILLS_HOME/<skill_name>/SKILL.md:
Metadata: Increment the chapter count, update the estimated page count, and add the new source names if appropriate. Update the Generated date to the current date.
Core Frameworks: Fold in the most high-impact mental models or principles from the new content (ensuring the overall file remains under 4,000 tokens).
Chapter Index: Append the new chapters to the index table, linking to the newly created files.
Topic Index: Merge the new topics alphabetically. If an existing topic is also covered in the new chapters, append the new chapter links to its line (e.g. - **Topic** → ch05, ch13).
6. Scan, Cleanup, and Report
Once the files are successfully written and merged, run Step 9.5, then proceed to Step 10 to perform cleanup and print a custom update report summarizing the newly added chapters, merged glossary terms, and updated indices. If the skill folder is a git repository with a remote (published via Step 11), offer to commit the update and push it.
Quality Rules
Extract structure, not summaries — capture named frameworks, exact formulations, anti-patterns; not chapter recaps
Preserve the author's precision — "The 5 Whys" ≠ "ask why multiple times"; keep exact naming
Density over completeness — a 1,000-token summary beats a 10,000-token excerpt
Practitioner voice — write "Use X when Y", not "The book explains X"
Front-load SKILL.md — compaction keeps the first 5,000 tokens; most important content comes first
Chapter files are on-demand — they don't count against skill budget until loaded
Never copy raw book text — always synthesize, summarize, extract signal
Topic index is critical — it's how the agent navigates to the right chapter file
Connect to the AI Agents Listing MCP server at https://aiagentslisting.com/api/mcp using the HTTP transport and complete the sign-in when it asks. Then call the submit_review tool for the listing "book-to-skill" with a rating from 1 to 5 and a short note on what worked and what did not.
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